Latent space models for multiplex networks with shared structure


Event details

Date 29.10.2021 15:1517:00  
Speaker Elizaveta (Liza) Levina, University of Michigan
Location Online
Category Conferences - Seminars
Event Language English

Latent space models are frequently used for modelling single-layer networks and include many popular special cases, such as the stochastic block model and the random dot product graph.   Yet in practice, more complex network structures are becoming increasingly common.  Here we propose a new latent space model for multiplex networks: multiple, heterogeneous networks observed on a shared node set. Multiplex networks can represent a network sample with shared node labels, a network evolving over time, or a single network with multiple types of edges.
The key feature of our model is that it learns from data how much of the network structure is shared between layers, and pools information across layers as appropriate. We establish identifiability, develop a fitting procedure using convex optimization in combination with a nuclear norm penalty, and prove a guarantee of recovery for the latent positions as long as there is sufficient separation between the shared and the individual latent subspaces. 
The new model compares favorably to other methods in the literature on simulated networks and on a multiplex network describing the worldwide trade of agricultural products. 

Joint work with Peter MacDonald and Ji Zhu.  

Practical information

  • Informed public
  • Free


  • Sofia Olhede


  • Maroussia Schaffner